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Glama

check_master_numbers

Detect master numbers (11, 22, 33) in a list, returning which ones appear and their total count for numerology analysis.

Instructions

Report the master numbers (11, 22, 33) among a list of numbers.

master_numbers lists the distinct master numbers found, ascending, and count how many occurrences there were: [11, 11] gives has_master=True, master_numbers=(11,) and count=2. An empty list is a valid input and reports no master number. Pure logic: no configuration, no service.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numbersYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
has_masterYes
master_numbersYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does disclose that this is pure logic with no configuration, no service, and therefore no side effects or side-channel dependencies. The empty-list behavior and the deterministic counting semantics (duplicates counted) are also stated, giving a fairly complete behavioral picture for a pure function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the purpose in one sentence, then adds the output-shape example and edge case. The example is dense but earns its place by pinning down counting semantics; no filler sentences are present.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be re-explained, yet the description still usefully illustrates the result shape and the duplicate-counting rule. Combined with the pure-logic note and empty-input case, it covers everything needed to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single parameter is documented only as 'array of integers' in the schema, so the description must compensate. It does so meaningfully by specifying that duplicates are counted (the [11,11] -> count=2 example) and that an empty array is accepted, clarifying semantics the schema omits.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (report) and resource (master numbers 11, 22, 33) and defines the domain concept, which separates it from generic siblings like compute_numerology or find_patterns. However, it never explicitly contrasts itself with those alternatives, so an agent must infer the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to pick this tool over siblings such as compute_numerology or find_patterns, nor any stated preconditions. The only usage hint is that an empty list is valid input, which is an edge case rather than selection guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.